Japanese investment in startup companies is surging, driven by interest in a number of powerful new technological trends. In the first six months of 2016, unlisted startups in the country raised a record 92.8 billion yen ( 897 million) of capital, up 21% from the same period the previous year. Total investment in entrepreneurial ventures in 2016 is likely to reach the highest level since data started being compiled in 2006. The trend has been led by companies keen to invest in ventures which own cutting-edge technology in areas like artificial intelligence. Japan Venture Research, a Tokyo-based research company, has estimated the amount of money raised by some 8,600 unlisted companies by analyzing data concerning their capital.

The HR Technology Conference is in Chicago this year from October 4-7 and I'll once again be blogging live from the show. As I'm preparing and scheduling meetings with various vendors one thing have become perfectly clear, I'll be doing a lot of talking about "Artificial Intelligence"(AI). The stuff we see in movies in the future where computers and robots begin the think for themselves then very quickly understand that humans are inadequate so they'decide' humans are no longer needed and only machines should run the world. Yeah, That AI! Sounds like the perfect HR replacement! The actual definition of Artificial Intelligence is simply, intelligence exhibited by machines.

Benevolent.ai, a British artificial intelligence (AI) healthcare company, has hired IBM's AI expert Jérôme Pesenti, ex-VP of Watson Core Technology, to head up its technology division. Founded in 2013, benevolent.ai was spun out of the management team working at Proximagen, a pharmaceutical company, who were frustrated with the slow pace of drug discovery. Data from patient databases and scientific papers is constantly expanding, said Ken Mulvany, chairman of benevolent.ai. Some of the information might be true, some of it will be speculative, and some of it will be false. The human brain just doesn't have the capacity to keep up," Mulvany told The Register. Benevolent.ai aims to analyse data and form connections through a knowledge graph, allowing researchers to observe patterns they might have missed. "The data might show that a protein upregulates a particular gene.

This post is Part 4 of the Deep Learning in a Nutshell series, in which I'll dive into reinforcement learning, a type of machine learning in which agents take actions in an environment aimed at maximizing their cumulative reward. Deep Learning in a Nutshell posts offer a high-level overview of essential concepts in deep learning. The posts aim to provide an understanding of each concept rather than its mathematical and theoretical details. While mathematical terminology is sometimes necessary and can further understanding, these posts use analogies and images whenever possible to provide easily digestible bits that make up an intuitive overview of the field of deep learning. Previous posts covered core concepts in deep learning, training of deep learning networks and their history, and sequence learning. Remember how you learned to ride a bike? More than likely an adult stood or walked behind you and encouraged you to make the first moves on your bike, and helped you get going again when you stumbled or fell. But it is very difficult to explain to a child how to ride a bike, and even a good explanation makes little sense to someone who has never ridden before: you have to get the feel for it. So how did you learn to ride a bike if it could not be clearly explained?

Microsoft is in an extraordinary situation right now. Unlike most of its competitors, Microsoft's business is incredibly diversified as Redmond transitions from its heyday of the PC era into the mobile first, cloud first paradigm. Microsoft's flexibility allows it to not be wed to any single revenue master in the same way that Apple (the iPhone) and Google (advertising) are with their primary income drivers. Microsoft has three primary business units--devices, cloud and productivity--that are all growing and all tied together like the most expensive Venn diagram of all time. About half of all of Microsoft's business in the last quarter ( 12.7 billion) was from its "More Personal Computing" reporting group which includes Windows, Bing and its own hardware like the Surface tablets and Xbox. Microsoft's Office-based "Productivity And Business" segment made 6.6 billion in revenue while its "Intelligent Cloud" made 6.34 billion.

This robot dives to depths humans dare not attempt - and it can bring people along for the ride without them getting wet. The Stanford-built OceanOne is filled with compressible oil to offset the crushing pressures experienced when 100 metres underwater, and AI-assisted navigation steers it clear of obstacles. Its operators remain on land, observing on screen everything the robot captures, using joysticks to drive it and guiding its hands through a feedback mechanism that relays tactile sensations. "It's impossible to let a robot act alone in such an environment: it will fail," says Professor Oussama Khatib, OceanOne's creator. "The only way you can guarantee success is connecting a worker through a haptic device to the robot.

For every great advance in artificial intelligence (AI), offering a chance for machines to truly think and be more useful than ever before, there is always a negative story supporting the fear that humans may one day be subservient to their creations. A recent online beauty pageant with 600,000 entrants using an AI judging process was blasted by critics when almost all the winners were white. Of course the AI system needs to learn from how someone defines beauty before it can start teaching itself from additional examples. In this case the vast majority of images that were initially used to define beauty were images of white people. Skin tone was not a factor in the judging process, but with the desirable attributes mostly defined as those more common in white people it's hardly a surprise that the AI system effectively learned how to be racist.

Whilst I can't fully refute either scenario, I can speculate on the potential for near-future political applications of AI which have the potential to be both disruptive and unintuitively desirable. Automatic, near-instant, ubiquitous fact checking UK fact-checking organisation Full Fact recently released a report that covered the state of various fact checking technologies in use and in development. It covered areas like overall existing semantic data standards in use and their challenges, and trends seem to be that natural language and contextual language recognition is going from strength to strength. The techniques in play include quickly responding to previously debunked claims, data mining statistics on demand and drawing confidence-based correlations from less reliable but plentiful data like news and social media reports. The convergent goal of these various projects appears to be moving fact checking from a post-hoc arm-chair analyst world, to a force that for example, in the middle of a debate or news report, is avalible to instantly challenge, rate or question any given factual statement within seconds of it being said.

Artificial intelligence interesting facts - is the topic for endless discussions. It is out of the discussion; we are futurists. And fantasies about the future, about the amazing things, such as "thinking computers" are always fascinating. But at the same time, there is something frightening about the issue. Artificial Intelligence interesting facts are on this subject as follows.

I've been putting out more JavaScript courses, playing with new technologies, and I got really deep into React and Redux. Been having a lot of fun with those. Redux introduces a more functional approach to JavaScript state management, which is pretty amazing. I've enjoyed building it into my projects, and it has really simplified things like unit testing for your app's state management. I really appreciate the simplicity of Reducers in Redux. If you're not familiar with Redux, the reducers are regular reducer functions for the purposes of app state management. Using reducers for state management has been really amazing and has totally transformed the way I think about building apps, so I've been really happy with it. I've also played with Angular 2 just a little and I've been playing with TypeScript. I have mixed feelings about both of those so far. Angular 2, compared to React and Redux, feels like it has a lot more overhead and not a lot of benefit. Like writing unit tests for the views in Angular 2 is much more complicated than writing unit tests for things like Pure Components in React. Maybe as I use it more I'll start to "feel" it a little more. In TypeScript, I really like the IDE(Integrated development environment) type hinting that it gives you, and I like the Type Inference capability so you don't have to manually annotate everything, which is fantastic ... an amazing feature. I like that more than the type annotations available with Tern.js, but the problem is that since it needs to see how the types flow through the program, sometimes it will infer types that are a little too strict and you have to go in and manually loosen up the type annotations. Sometimes, it's really hard to do that especially if you use any kind of complicated functional programming techniques, which I tend to do once in a while.